Senior Engineering Manager, ML Platform

The Machine Learning team — internally known as "Potato Radius" — builds the training pipelines, feature infrastructure, and evaluation systems behind every score Sift returns, across more than 700 customers and a trillion-plus events a year. We are Sift's Data Science and ML Engineering team responsible to ship models fast, prove they work, and trust them in production.

- Lead and grow the team: Own the roadmap, execution, and quality of the systems that train, evaluate, and serve Sift's ML models in production, leading a team of ML platform engineers and data scientists.

- Stay technical: Review designs, unblock engineers on hard problems, and make credible calls on architecture and trade-offs.

- Drive customer POVs: Partner directly with strategic customers and Sales/Solutions Engineering on technical proof-of-value engagements, translating customer requirements into platform capabilities.

- Reduce technical debt: Drive a sustained, measurable reduction in technical debt across the ML platform, balancing new feature delivery with the health of existing systems.

- Build evaluation frameworks: Mature the systems that give Data Science and ML Engineering fast, trustworthy signals on model quality before and after deployment.

- Automate the ML lifecycle: Identify repeatable, manual processes across training, evaluation, deployment, and monitoring, and drive their automation.

- Partner cross-functionally: Align platform investments with business priorities alongside Data Science, Core Infrastructure, Product, and Customer Success.

TECHNICAL STACK

GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python

WHAT WOULD MAKE YOU A STRONG FIT

- 8+ years of overall hands-on engineering experience, including 4+ years managing software, data science or machine learning engineering teams.

- Experience managing Data Scientist and/or in-depth knowledge for data science.

- Deep technical fluency in machine learning systems: model training pipelines, feature engineering, model serving, and evaluation at production scale.

- Proven track record leading technical customer engagements or POVs, including direct interaction with enterprise customers.

- Demonstrated success reducing technical debt in a live, high-traffic production system without stalling feature delivery.

- Experience designing or scaling evaluation frameworks (offline and/or online) for machine learning models.

- Track record of identifying manual, repeatable engineering processes and driving their automation.

- Experience hiring, mentoring, and developing engineering talent.

- B.S. or MS/Phd in Computer Science (or related technical discipline), or equivalent practical experience.

We're hiring a Senior Engineering Manager to lead this team. You're a manager who's inspiring and technical, and who knows how to bring focus to what matters now without losing sight of the long term. You value collaboration and transparency, operate with a get-stuff-done mindset, and bring the technical depth and bias for shipping to spot the manual, brittle, or duplicated work that's quietly slowing the team down. You build a culture of mentorship, give regular and constructive feedback, set clear goals, and grow your team by hiring effectively.

PROJECTS YOU MIGHT LEAD

- Launch a unified model evaluation framework that gives Data Science fast, trustworthy, apples-to-apples comparisons before a model ever reaches production or shadow traffic.

- Evolve core feature infrastructure — including a new global feature store — to improve accuracy and unlock faster experimentation.

- Bring a fresh approach to model configuration, replacing tribal knowledge and manual gating with auditable, safely-controlled releases.

- Introduce agentic, AI-assisted tooling into customer investigations, automating repetitive data pulls and validation so analysts spend their time on judgment calls, not manual digging.

- Build automation that detects an active fraud attack, adjusts score calibration in real time, and cleanly reverts once it subsides.

- Experience with large-scale distributed ML infrastructure such as Spark, Flink, Databricks, or similar.

- Familiarity with fraud detection, risk, or trust & safety domains.

- Hands-on experience with GCP or AWS ML infrastructure.

- Experience with streaming architectures (e.g., Kafka) and containerized/orchestrated deployments (Docker, Kubernetes).

- Familiarity with using AI coding assistants (e.g., Claude Code) to accelerate development.

- Competitive total compensation package

- 401k plan

- Medical, dental and vision coverage

- Wellness reimbursement

- Education reimbursement

- Flexible time off

Let’s build it together

At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.

This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy